Biomarkers of Systemic Inflammation in Ugandan Infants and Children Hospitalized With Respiratory Syncytial Virus Infection
Bibliographic record
Abstract
BACKGROUND: Optimizing outcomes in respiratory syncytial virus (RSV) pneumonia requires accurate diagnosis and determination of severity that, in resource-limited settings, is often based on clinical assessment alone. We describe host inflammatory biomarkers and clinical outcomes among children hospitalized with RSV lower respiratory tract infection (LRTI) in Uganda and controls with rhinovirus and pneumococcal pneumonia. METHODS: 58 children hospitalized with LRTI were included. We compared 37 patients with RSV, 10 control patients with rhinovirus and 11 control patients with suspected pneumococcal pneumonia. RESULTS: Patients in the RSV group had significantly lower levels of C-reactive protein (CRP) and chitinase-3-like protein 1 (CHI3L1) than the pneumococcal pneumonia group (P < 0.05 for both). Among children with RSV, higher admission levels of CRP predicted prolonged time to resolution of tachypnea, tachycardia and fever. Higher levels of CHI3L1 were associated with higher composite clinical severity scores and predicted prolonged time to resolution of tachypnea and tachycardia, time to wean oxygen and time to sit. Higher levels of lipocalin-2 (LCN2) predicted prolonged time to resolution of tachypnea, tachycardia and time to feed. Higher admission levels of all 3 biomarkers were predictive of a higher total volume of oxygen administered during hospitalization (P < 0.05 for all comparisons). Of note, CHI3L1 and LCN2 appeared to predict clinical outcomes more accurately than CRP, the inflammatory biomarker most widely used in clinical practice. CONCLUSIONS: Our findings suggest that CHI3L1 and LCN2 may be clinically informative biomarkers in childhood RSV LRTI in low-resource settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".